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ORCIDTom Goldstein
Thomas A. Goldstein
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- affiliation: University of Maryland, Department of Computer Science, College Park, MD, USA
- affiliation (PhD 2010): University of California, Los Angeles, CA, USA
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2020 – today
- 2023
[j17]Micah Goldblum
, Dimitris Tsipras, Chulin Xie, Xinyun Chen, Avi Schwarzschild, Dawn Song, Aleksander Madry, Bo Li, Tom Goldstein:
Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses. IEEE Trans. Pattern Anal. Mach. Intell. 45(2): 1563-1580 (2023)- [i161]John Kirchenbauer, Jonas Geiping, Yuxin Wen, Jonathan Katz, Ian Miers, Tom Goldstein:
A Watermark for Large Language Models. CoRR abs/2301.10226 (2023) - [i160]Yuancheng Xu, Yanchao Sun, Micah Goldblum, Tom Goldstein, Furong Huang:
Exploring and Exploiting Decision Boundary Dynamics for Adversarial Robustness. CoRR abs/2302.03015 (2023) - [i159]Yuxin Wen, Neel Jain, John Kirchenbauer, Micah Goldblum, Jonas Geiping, Tom Goldstein:
Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery. CoRR abs/2302.03668 (2023) - [i158]Arpit Bansal, Hong-Min Chu, Avi Schwarzschild, Soumyadip Sengupta, Micah Goldblum, Jonas Geiping, Tom Goldstein:
Universal Guidance for Diffusion Models. CoRR abs/2302.07121 (2023) - [i157]Alex Stein, Avi Schwarzschild, Michael J. Curry, Tom Goldstein, John P. Dickerson:
Neural Auctions Compromise Bidder Information. CoRR abs/2303.00116 (2023) - 2022
- [j16]Haochuan Song, Tom Goldstein, Xiaohu You, Chuan Zhang, Olav Tirkkonen, Christoph Studer:
Joint Channel Estimation and Data Detection in Cell-Free Massive MU-MIMO Systems. IEEE Trans. Wirel. Commun. 21(6): 4068-4084 (2022) - [c107]Zhipeng Wei, Jingjing Chen, Micah Goldblum, Zuxuan Wu, Tom Goldstein, Yu-Gang Jiang:
Towards Transferable Adversarial Attacks on Vision Transformers. AAAI 2022: 2668-2676 - [c106]Michael J. Curry, Uro Lyi, Tom Goldstein, John P. Dickerson:
Learning Revenue-Maximizing Auctions With Differentiable Matching. AISTATS 2022: 6062-6073 - [c105]Kezhi Kong, Guohao Li, Mucong Ding, Zuxuan Wu, Chen Zhu, Bernard Ghanem, Gavin Taylor, Tom Goldstein:
Robust Optimization as Data Augmentation for Large-scale Graphs. CVPR 2022: 60-69 - [c104]Pedro Sandoval Segura, Vasu Singla, Liam Fowl, Jonas Geiping, Micah Goldblum, David Jacobs, Tom Goldstein:
Poisons that are learned faster are more effective. CVPR Workshops 2022: 197-204 - [c103]Gowthami Somepalli, Liam Fowl, Arpit Bansal, Ping-Yeh Chiang, Yehuda Dar, Richard G. Baraniuk, Micah Goldblum, Tom Goldstein:
Can Neural Nets Learn the Same Model Twice? Investigating Reproducibility and Double Descent from the Decision Boundary Perspective. CVPR 2022: 13689-13698 - [c102]Jiuhai Chen, Jonas Mueller, Vassilis N. Ioannidis, Soji Adeshina, Yangkun Wang, Tom Goldstein, David Wipf:
Does your graph need a confidence boost? Convergent boosted smoothing on graphs with tabular node features. ICLR 2022 - [c101]Liam H. Fowl, Jonas Geiping, Wojciech Czaja, Micah Goldblum, Tom Goldstein:
Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models. ICLR 2022 - [c100]Jonas Geiping, Micah Goldblum, Phillip Pope, Michael Moeller, Tom Goldstein:
Stochastic Training is Not Necessary for Generalization. ICLR 2022 - [c99]Renkun Ni, Manli Shu, Hossein Souri, Micah Goldblum, Tom Goldstein:
The Close Relationship Between Contrastive Learning and Meta-Learning. ICLR 2022 - [c98]Avi Schwarzschild, Arjun Gupta, Amin Ghiasi, Micah Goldblum, Tom Goldstein:
The Uncanny Similarity of Recurrence and Depth. ICLR 2022 - [c97]Chen Zhu, Zheng Xu, Mingqing Chen, Jakub Konecný, Andrew Hard, Tom Goldstein:
Diurnal or Nocturnal? Federated Learning of Multi-branch Networks from Periodically Shifting Distributions. ICLR 2022 - [c96]Arpit Bansal, Ping-Yeh Chiang, Michael J. Curry, Rajiv Jain, Curtis Wigington, Varun Manjunatha, John P. Dickerson, Tom Goldstein:
Certified Neural Network Watermarks with Randomized Smoothing. ICML 2022: 1450-1465 - [c95]Amin Ghiasi, Hamid Kazemi, Steven Reich, Chen Zhu, Micah Goldblum, Tom Goldstein:
Plug-In Inversion: Model-Agnostic Inversion for Vision with Data Augmentations. ICML 2022: 7484-7512 - [c94]Yuxin Wen, Jonas Geiping, Liam Fowl, Micah Goldblum, Tom Goldstein:
Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification. ICML 2022: 23668-23684 - [i156]Harrison Foley, Liam Fowl, Tom Goldstein, Gavin Taylor:
Execute Order 66: Targeted Data Poisoning for Reinforcement Learning. CoRR abs/2201.00762 (2022) - [i155]Samuel Dooley, George Z. Wei, Tom Goldstein, John P. Dickerson:
Are Commercial Face Detection Models as Biased as Academic Models? CoRR abs/2201.10047 (2022) - [i154]Aounon Kumar, Alexander Levine, Tom Goldstein, Soheil Feizi:
Certifying Model Accuracy under Distribution Shifts. CoRR abs/2201.12440 (2022) - [i153]Liam Fowl, Jonas Geiping, Steven Reich, Yuxin Wen, Wojtek Czaja, Micah Goldblum, Tom Goldstein:
Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models. CoRR abs/2201.12675 (2022) - [i152]Amin Ghiasi, Hamid Kazemi, Steven Reich, Chen Zhu, Micah Goldblum, Tom Goldstein:
Plug-In Inversion: Model-Agnostic Inversion for Vision with Data Augmentations. CoRR abs/2201.12961 (2022) - [i151]Yuxin Wen, Jonas Geiping, Liam Fowl, Micah Goldblum, Tom Goldstein:
Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification. CoRR abs/2202.00580 (2022) - [i150]Arpit Bansal, Avi Schwarzschild, Eitan Borgnia, Zeyad Emam, Furong Huang, Micah Goldblum, Tom Goldstein:
End-to-end Algorithm Synthesis with Recurrent Networks: Logical Extrapolation Without Overthinking. CoRR abs/2202.05826 (2022) - [i149]Gowthami Somepalli, Liam Fowl, Arpit Bansal, Ping-Yeh Chiang, Yehuda Dar, Richard G. Baraniuk, Micah Goldblum, Tom Goldstein:
Can Neural Nets Learn the Same Model Twice? Investigating Reproducibility and Double Descent from the Decision Boundary Perspective. CoRR abs/2203.08124 (2022) - [i148]Valeriia Cherepanova, Steven Reich, Samuel Dooley, Hossein Souri, Micah Goldblum, Tom Goldstein:
A Deep Dive into Dataset Imbalance and Bias in Face Identification. CoRR abs/2203.08235 (2022) - [i147]Pedro Sandoval Segura, Vasu Singla, Liam Fowl, Jonas Geiping, Micah Goldblum, David Jacobs, Tom Goldstein:
Poisons that are learned faster are more effective. CoRR abs/2204.08615 (2022) - [i146]Pedro Sandoval Segura, Vasu Singla, Jonas Geiping, Micah Goldblum, Tom Goldstein, David W. Jacobs:
Autoregressive Perturbations for Data Poisoning. CoRR abs/2206.03693 (2022) - [i145]Jiuhai Chen, Jonas Mueller, Vassilis N. Ioannidis, Tom Goldstein, David Wipf:
A Robust Stacking Framework for Training Deep Graph Models with Multifaceted Node Features. CoRR abs/2206.08473 (2022) - [i144]Roman Levin, Valeriia Cherepanova, Avi Schwarzschild, Arpit Bansal, C. Bayan Bruss, Tom Goldstein, Andrew Gordon Wilson, Micah Goldblum:
Transfer Learning with Deep Tabular Models. CoRR abs/2206.15306 (2022) - [i143]Arpit Bansal, Ping-yeh Chiang, Michael J. Curry, Rajiv Jain, Curtis Wigington, Varun Manjunatha, John P. Dickerson, Tom Goldstein:
Certified Neural Network Watermarks with Randomized Smoothing. CoRR abs/2207.07972 (2022) - [i142]Arpit Bansal, Eitan Borgnia, Hong-Min Chu, Jie S. Li, Hamid Kazemi, Furong Huang, Micah Goldblum, Jonas Geiping, Tom Goldstein:
Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise. CoRR abs/2208.09392 (2022) - [i141]Manli Shu, Weili Nie, De-An Huang, Zhiding Yu, Tom Goldstein, Anima Anandkumar, Chaowei Xiao:
Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models. CoRR abs/2209.07511 (2022) - [i140]Jonas Geiping, Micah Goldblum, Gowthami Somepalli, Ravid Shwartz-Ziv, Tom Goldstein, Andrew Gordon Wilson:
How Much Data Are Augmentations Worth? An Investigation into Scaling Laws, Invariance, and Implicit Regularization. CoRR abs/2210.06441 (2022) - [i139]Yuxin Wen, Jonas Geiping, Liam Fowl, Hossein Souri, Rama Chellappa, Micah Goldblum, Tom Goldstein:
Thinking Two Moves Ahead: Anticipating Other Users Improves Backdoor Attacks in Federated Learning. CoRR abs/2210.09305 (2022) - [i138]Yuxin Wen, Arpit Bansal, Hamid Kazemi, Eitan Borgnia, Micah Goldblum, Jonas Geiping, Tom Goldstein:
Canary in a Coalmine: Better Membership Inference with Ensembled Adversarial Queries. CoRR abs/2210.10750 (2022) - [i137]Renkun Ni, Ping-yeh Chiang, Jonas Geiping, Micah Goldblum, Andrew Gordon Wilson, Tom Goldstein:
K-SAM: Sharpness-Aware Minimization at the Speed of SGD. CoRR abs/2210.12864 (2022) - [i136]Samuel Dooley, George Z. Wei, Tom Goldstein, John P. Dickerson:
Robustness Disparities in Face Detection. CoRR abs/2211.15937 (2022) - [i135]Gowthami Somepalli, Vasu Singla, Micah Goldblum, Jonas Geiping, Tom Goldstein:
Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models. CoRR abs/2212.03860 (2022) - [i134]Amin Ghiasi, Hamid Kazemi, Eitan Borgnia, Steven Reich, Manli Shu, Micah Goldblum, Andrew Gordon Wilson, Tom Goldstein:
What do Vision Transformers Learn? A Visual Exploration. CoRR abs/2212.06727 (2022) - [i133]Jonas Geiping, Tom Goldstein:
Cramming: Training a Language Model on a Single GPU in One Day. CoRR abs/2212.14034 (2022) - 2021
- [c93]Huimin Zeng, Chen Zhu, Tom Goldstein, Furong Huang:
Are Adversarial Examples Created Equal? A Learnable Weighted Minimax Risk for Robustness under Non-uniform Attacks. AAAI 2021: 10815-10823 - [c92]Gian Marti, Oscar Castañeda, Sven Jacobsson, Giuseppe Durisi, Tom Goldstein, Christoph Studer:
Hybrid Jammer Mitigation for All-Digital mmWave Massive MU-MIMO. ACSCC 2021: 93-99 - [c91]Micah Goldblum, Avi Schwarzschild, Ankit B. Patel, Tom Goldstein:
Adversarial attacks on machine learning systems for high-frequency trading. ICAIF 2021: 2:1-2:9 - [c90]Eitan Borgnia, Valeriia Cherepanova, Liam Fowl, Amin Ghiasi, Jonas Geiping, Micah Goldblum, Tom Goldstein, Arjun Gupta:
Strong Data Augmentation Sanitizes Poisoning and Backdoor Attacks Without an Accuracy Tradeoff. ICASSP 2021: 3855-3859 - [c89]Valeriia Cherepanova, Micah Goldblum, Harrison Foley, Shiyuan Duan, John P. Dickerson, Gavin Taylor, Tom Goldstein:
LowKey: Leveraging Adversarial Attacks to Protect Social Media Users from Facial Recognition. ICLR 2021 - [c88]Jonas Geiping, Liam H. Fowl, W. Ronny Huang, Wojciech Czaja, Gavin Taylor, Michael Moeller, Tom Goldstein:
Witches' Brew: Industrial Scale Data Poisoning via Gradient Matching. ICLR 2021 - [c87]Renkun Ni, Hong-Min Chu, Oscar Castañeda, Ping-yeh Chiang, Christoph Studer, Tom Goldstein:
WrapNet: Neural Net Inference with Ultra-Low-Precision Arithmetic. ICLR 2021 - [c86]Phillip Pope, Chen Zhu, Ahmed Abdelkader, Micah Goldblum, Tom Goldstein:
The Intrinsic Dimension of Images and Its Impact on Learning. ICLR 2021 - [c85]Renkun Ni, Micah Goldblum, Amr Sharaf, Kezhi Kong, Tom Goldstein:
Data Augmentation for Meta-Learning. ICML 2021: 8152-8161 - [c84]Avi Schwarzschild, Micah Goldblum, Arjun Gupta, John P. Dickerson, Tom Goldstein:
Just How Toxic is Data Poisoning? A Unified Benchmark for Backdoor and Data Poisoning Attacks. ICML 2021: 9389-9398 - [c83]Manli Shu, Yu Shen, Ming C. Lin, Tom Goldstein:
Adversarial Differentiable Data Augmentation for Autonomous Systems. ICRA 2021: 14069-14075 - [c82]Aounon Kumar, Tom Goldstein:
Center Smoothing: Certified Robustness for Networks with Structured Outputs. NeurIPS 2021: 5560-5575 - [c81]Avi Schwarzschild, Eitan Borgnia, Arjun Gupta, Furong Huang, Uzi Vishkin, Micah Goldblum, Tom Goldstein:
Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks. NeurIPS 2021: 6695-6706 - [c80]Mucong Ding, Kezhi Kong, Jingling Li, Chen Zhu, John Dickerson, Furong Huang, Tom Goldstein:
VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization. NeurIPS 2021: 6733-6746 - [c79]Chen Zhu, Renkun Ni, Zheng Xu, Kezhi Kong, W. Ronny Huang, Tom Goldstein:
GradInit: Learning to Initialize Neural Networks for Stable and Efficient Training. NeurIPS 2021: 16410-16422 - [c78]Chen Zhu, Wei Ping, Chaowei Xiao, Mohammad Shoeybi, Tom Goldstein, Anima Anandkumar, Bryan Catanzaro:
Long-Short Transformer: Efficient Transformers for Language and Vision. NeurIPS 2021: 17723-17736 - [c77]Yu Shen, Laura Y. Zheng, Manli Shu, Weizi Li, Tom Goldstein, Ming C. Lin:
Gradient-Free Adversarial Training Against Image Corruption for Learning-based Steering. NeurIPS 2021: 26250-26263 - [c76]Manli Shu, Zuxuan Wu, Micah Goldblum, Tom Goldstein:
Encoding Robustness to Image Style via Adversarial Feature Perturbations. NeurIPS 2021: 28042-28053 - [c75]Liam Fowl, Micah Goldblum, Ping-yeh Chiang, Jonas Geiping, Wojciech Czaja, Tom Goldstein:
Adversarial Examples Make Strong Poisons. NeurIPS 2021: 30339-30351 - [c74]Chen Zhu, Yu Cheng, Zhe Gan, Furong Huang, Jingjing Liu, Tom Goldstein:
MaxVA: Fast Adaptation of Step Sizes by Maximizing Observed Variance of Gradients. ECML/PKDD (3) 2021: 628-643 - [i132]Valeriia Cherepanova, Micah Goldblum, Harrison Foley, Shiyuan Duan, John P. Dickerson, Gavin Taylor, Tom Goldstein:
LowKey: Leveraging Adversarial Attacks to Protect Social Media Users from Facial Recognition. CoRR abs/2101.07922 (2021) - [i131]Valeriia Cherepanova, Vedant Nanda, Micah Goldblum, John P. Dickerson, Tom Goldstein:
Technical Challenges for Training Fair Neural Networks. CoRR abs/2102.06764 (2021) - [i130]Chen Zhu, Renkun Ni, Zheng Xu, Kezhi Kong, W. Ronny Huang, Tom Goldstein:
GradInit: Learning to Initialize Neural Networks for Stable and Efficient Training. CoRR abs/2102.08098 (2021) - [i129]Aounon Kumar, Tom Goldstein:
Center Smoothing for Certifiably Robust Vector-Valued Functions. CoRR abs/2102.09701 (2021) - [i128]Avi Schwarzschild, Arjun Gupta, Micah Goldblum, Tom Goldstein:
Thinking Deeply with Recurrence: Generalizing from Easy to Hard Sequential Reasoning Problems. CoRR abs/2102.11011 (2021) - [i127]Yu Shen, Laura Y. Zheng, Manli Shu, Weizi Li, Tom Goldstein, Ming C. Lin:
Improving Robustness of Learning-based Autonomous Steering Using Adversarial Images. CoRR abs/2102.13262 (2021) - [i126]Jonas Geiping, Liam Fowl, Gowthami Somepalli, Micah Goldblum, Michael Moeller, Tom Goldstein:
What Doesn't Kill You Makes You Robust(er): Adversarial Training against Poisons and Backdoors. CoRR abs/2102.13624 (2021) - [i125]Eitan Borgnia, Jonas Geiping, Valeriia Cherepanova, Liam Fowl, Arjun Gupta, Amin Ghiasi, Furong Huang, Micah Goldblum, Tom Goldstein:
DP-InstaHide: Provably Defusing Poisoning and Backdoor Attacks with Differentially Private Data Augmentations. CoRR abs/2103.02079 (2021) - [i124]Liam Fowl, Ping-yeh Chiang, Micah Goldblum, Jonas Geiping, Arpit Bansal, Wojtek Czaja, Tom Goldstein:
Preventing Unauthorized Use of Proprietary Data: Poisoning for Secure Dataset Release. CoRR abs/2103.02683 (2021) - [i123]Chen Chen, Kezhi Kong, Peihong Yu, Juan Luque, Tom Goldstein, Furong Huang:
Insta-RS: Instance-wise Randomized Smoothing for Improved Robustness and Accuracy. CoRR abs/2103.04436 (2021) - [i122]Shivam Akhauri, Laura Y. Zheng, Tom Goldstein, Ming C. Lin:
Improving Generalization of Transfer Learning Across Domains Using Spatio-Temporal Features in Autonomous Driving. CoRR abs/2103.08116 (2021) - [i121]Zuxuan Wu, Tom Goldstein, Larry S. Davis, Ser-Nam Lim:
THAT: Two Head Adversarial Training for Improving Robustness at Scale. CoRR abs/2103.13612 (2021) - [i120]Phillip Pope, Chen Zhu, Ahmed Abdelkader, Micah Goldblum, Tom Goldstein:
The Intrinsic Dimension of Images and Its Impact on Learning. CoRR abs/2104.08894 (2021) - [i119]Gowthami Somepalli, Micah Goldblum, Avi Schwarzschild, C. Bayan Bruss, Tom Goldstein:
SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training. CoRR abs/2106.01342 (2021) - [i118]Avi Schwarzschild, Eitan Borgnia, Arjun Gupta, Furong Huang, Uzi Vishkin, Micah Goldblum, Tom Goldstein:
Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks. CoRR abs/2106.04537 (2021) - [i117]Michael J. Curry, Uro Lyi, Tom Goldstein, John Dickerson:
Learning Revenue-Maximizing Auctions With Differentiable Matching. CoRR abs/2106.07877 (2021) - [i116]Hossein Souri, Micah Goldblum, Liam Fowl, Rama Chellappa, Tom Goldstein:
Sleeper Agent: Scalable Hidden Trigger Backdoors for Neural Networks Trained from Scratch. CoRR abs/2106.08970 (2021) - [i115]Arpit Bansal, Micah Goldblum, Valeriia Cherepanova, Avi Schwarzschild, C. Bayan Bruss, Tom Goldstein:
MetaBalance: High-Performance Neural Networks for Class-Imbalanced Data. CoRR abs/2106.09643 (2021) - [i114]Liam Fowl, Micah Goldblum, Ping-yeh Chiang, Jonas Geiping, Wojtek Czaja, Tom Goldstein:
Adversarial Examples Make Strong Poisons. CoRR abs/2106.10807 (2021) - [i113]Chen Zhu, Wei Ping, Chaowei Xiao, Mohammad Shoeybi, Tom Goldstein, Anima Anandkumar, Bryan Catanzaro:
Long-Short Transformer: Efficient Transformers for Language and Vision. CoRR abs/2107.02192 (2021) - [i112]Roman Levin, Manli Shu, Eitan Borgnia, Furong Huang, Micah Goldblum, Tom Goldstein:
Where do Models go Wrong? Parameter-Space Saliency Maps for Explainability. CoRR abs/2108.01335 (2021) - [i111]Avi Schwarzschild, Eitan Borgnia, Arjun Gupta, Arpit Bansal, Zeyad Emam, Furong Huang, Micah Goldblum, Tom Goldstein:
Datasets for Studying Generalization from Easy to Hard Examples. CoRR abs/2108.06011 (2021) - [i110]Samuel Dooley, Tom Goldstein, John P. Dickerson:
Robustness Disparities in Commercial Face Detection. CoRR abs/2108.12508 (2021) - [i109]Zhipeng Wei, Jingjing Chen, Micah Goldblum, Zuxuan Wu, Tom Goldstein, Yu-Gang Jiang:
Towards Transferable Adversarial Attacks on Vision Transformers. CoRR abs/2109.04176 (2021) - [i108]Jonas Geiping, Micah Goldblum, Phillip E. Pope, Michael Moeller, Tom Goldstein:
Stochastic Training is Not Necessary for Generalization. CoRR abs/2109.14119 (2021) - [i107]Samuel Dooley, Ryan Downing, George Z. Wei, Nathan Shankar, Bradon Thymes, Gudrun Thorkelsdottir, Tiye Kurtz-Miott, Rachel Mattson, Olufemi Obiwumi, Valeriia Cherepanova, Micah Goldblum, John P. Dickerson, Tom Goldstein:
Comparing Human and Machine Bias in Face Recognition. CoRR abs/2110.08396 (2021) - [i106]Liam Fowl, Jonas Geiping, Wojtek Czaja, Micah Goldblum, Tom Goldstein:
Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models. CoRR abs/2110.13057 (2021) - [i105]Jiuhai Chen, Jonas Mueller, Vassilis N. Ioannidis, Soji Adeshina, Yangkun Wang, Tom Goldstein, David Wipf:
Convergent Boosted Smoothing for Modeling Graph Data with Tabular Node Features. CoRR abs/2110.13413 (2021) - [i104]Mucong Ding, Kezhi Kong, Jingling Li, Chen Zhu, John P. Dickerson, Furong Huang, Tom Goldstein:
VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization. CoRR abs/2110.14363 (2021) - [i103]Haochuan Song, Tom Goldstein, Xiaohu You, Chuan Zhang, Olav Tirkkonen, Christoph Studer:
Joint Channel Estimation and Data Detection in Cell-Free Massive MU-MIMO Systems. CoRR abs/2110.15928 (2021) - [i102]Shishira R. Maiya, Max Ehrlich, Vatsal Agarwal, Ser-Nam Lim, Tom Goldstein, Abhinav Shrivastava:
A Frequency Perspective of Adversarial Robustness. CoRR abs/2111.00861 (2021) - [i101]Zeyad Ali Sami Emam, Hong-Min Chu, Ping-Yeh Chiang, Wojciech Czaja, Richard Leapman, Micah Goldblum, Tom Goldstein:
Active Learning at the ImageNet Scale. CoRR abs/2111.12880 (2021) - [i100]Gian Marti, Oscar Castañeda, Sven Jacobsson, Giuseppe Durisi, Tom Goldstein, Christoph Studer:
Hybrid Jammer Mitigation for All-Digital mmWave Massive MU-MIMO. CoRR abs/2111.13055 (2021) - 2020
- [j15]Oscar Castañeda, Sven Jacobsson, Giuseppe Durisi, Tom Goldstein, Christoph Studer:
Finite-Alphabet MMSE Equalization for All-Digital Massive MU-MIMO mmWave Communication. IEEE J. Sel. Areas Commun. 38(9): 2128-2141 (2020) - [j14]Oscar Castañeda, Sven Jacobsson, Giuseppe Durisi, Tom Goldstein, Christoph Studer:
High-Bandwidth Spatial Equalization for mmWave Massive MU-MIMO With Processing-in-Memory. IEEE Trans. Circuits Syst. II Express Briefs 67-II(5): 891-895 (2020) - [c73]Micah Goldblum, Liam Fowl, Soheil Feizi, Tom Goldstein:
Adversarially Robust Distillation. AAAI 2020: 3996-4003 - [c72]Ali Shafahi, Mahyar Najibi, Zheng Xu, John P. Dickerson, Larry S. Davis, Tom Goldstein:
Universal Adversarial Training. AAAI 2020: 5636-5643 - [c71]Oscar Castañeda, Sven Jacobsson, Giuseppe Durisi, Tom Goldstein, Christoph Studer:
Hardware-Friendly Two-Stage Spatial Equalization for All-Digital mmWave Massive MU-MIMO. ACSSC 2020: 388-392 - [c70]Abhay Kumar Yadav, Tom Goldstein, David W. Jacobs:
Making L-BFGS Work with Industrial-Strength Nets. BMVC 2020 - [c69]Ramina Ghods, Andrew S. Lan, Tom Goldstein, Christoph Studer:
MSE-Optimal Neural Network Initialization via Layer Fusion. CISS 2020: 1-6 - [c68]Zuxuan Wu, Ser-Nam Lim, Larry S. Davis, Tom Goldstein:
Making an Invisibility Cloak: Real World Adversarial Attacks on Object Detectors. ECCV (4) 2020: 1-17 - [c67]Neehar Peri, Neal Gupta, W. Ronny Huang, Liam Fowl, Chen Zhu, Soheil Feizi, Tom Goldstein, John P. Dickerson:
Deep k-NN Defense Against Clean-Label Data Poisoning Attacks. ECCV Workshops (1) 2020: 55-70 - [c66]Oscar Castañeda, Sven Jacobsson, Giuseppe Durisi, Tom Goldstein, Christoph Studer:
Soft-Output Finite Alphabet Equalization for mmWave Massive MIMO. ICASSP 2020: 1763-1767 - [c65]Ahmed Abdelkader, Michael J. Curry, Liam Fowl, Tom Goldstein, Avi Schwarzschild, Manli Shu, Christoph Studer, Chen Zhu:
Headless Horseman: Adversarial Attacks on Transfer Learning Models. ICASSP 2020: 3087-3091 - [c64]Ping-Yeh Chiang, Jonas Geiping, Micah Goldblum, Tom Goldstein, Renkun Ni, Steven Reich, Ali Shafahi:
Witchcraft: Efficient PGD Attacks with Random Step Size. ICASSP 2020: 3747-3751 - [c63]W. Ronny Huang, Zeyad Emam, Micah Goldblum, Liam Fowl, Justin K. Terry, Furong Huang, Tom Goldstein:
Understanding Generalization Through Visualizations. ICBINB@NeurIPS 2020: 87-97 - [c62]Ping-yeh Chiang, Renkun Ni, Ahmed Abdelkader, Chen Zhu, Christoph Studer, Tom Goldstein:
Certified Defenses for Adversarial Patches. ICLR 2020 - [c61]Amin Ghiasi, Ali Shafahi, Tom Goldstein:
Breaking Certified Defenses: Semantic Adversarial Examples with Spoofed robustness Certificates. ICLR 2020 - [c60]Micah Goldblum, Jonas Geiping, Avi Schwarzschild, Michael Moeller, Tom Goldstein:
Truth or backpropaganda? An empirical investigation of deep learning theory. ICLR 2020 - [c59]Ali Shafahi, Parsa Saadatpanah, Chen Zhu, Amin Ghiasi, Christoph Studer, David W. Jacobs, Tom Goldstein:
Adversarially robust transfer learning. ICLR 2020 - [c58]Chengxi Ye, Matthew Evanusa, Hua He, Anton Mitrokhin, Tom Goldstein, James A. Yorke, Cornelia Fermüller, Yiannis Aloimonos:
Network Deconvolution. ICLR 2020 - [c57]